does strengthening self-defense law deter crime or escalate violence? evidence from castle doctrine
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A study by the National Bureau of Economic ResearchTRANSCRIPT
NBER WORKING PAPER SERIES
DOES STRENGTHENING SELF-DEFENSE LAW DETER CRIME OR ESCALATEVIOLENCE? EVIDENCE FROM CASTLE DOCTRINE
Cheng ChengMark Hoekstra
Working Paper 18134http://www.nber.org/papers/w18134
NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue
Cambridge, MA 02138June 2012
We would like to thank Scott Cunningham, Steve Puller, Joanna Lahey, Erdal Tekin, Chandler McClellan,and Jonathan Meer for providing helpful comments and suggestions. We would like to thank MarkSeaman for providing excellent research assistance. The views expressed herein are those of the authorsand do not necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies officialNBER publications.
© 2012 by Cheng Cheng and Mark Hoekstra. All rights reserved. Short sections of text, not to exceedtwo paragraphs, may be quoted without explicit permission provided that full credit, including © notice,is given to the source.
Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence fromCastle DoctrineCheng Cheng and Mark HoekstraNBER Working Paper No. 18134June 2012, Revised June 2012JEL No. K0,K14
ABSTRACT
From 2000 to 2010, more than 20 states passed laws that make it easier to use lethal force in self-defense. Elements of these laws include removing the duty to retreat in places outside of one’s home, addinga presumption of reasonable belief of imminent harm, and removing civil liability for those actingunder the law. This paper examines whether aiding self-defense in this way deters crime or, alternatively,increases homicide. To do so, we apply a difference-in-differences research design by exploiting thewithin-state variation in law adoption. We find no evidence of deterrence; burglary, robbery, andaggravated assault are unaffected by the laws. On the other hand, we find that homicides are increasedby around 8 percent, and that these homicides are largely classified by police as murder. This suggeststhat a primary consequence of strengthened self-defense law is a net increase in homicide. Finally,we present back-of-the-envelope calculations using evidence on the relative increase in reported justifiablehomicide, along with assumptions about the degree and nature of underreporting, to assess whetherthe entire increase was legally justified.
Cheng ChengDepartment of EconomicsTexas A&M University4228 TAMUCollege Station, TX [email protected]
Mark HoekstraDepartment of EconomicsTexas A&M University3087 Allen Building4228 TAMUCollege Station, TX 77843and [email protected]
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1. Introduction
A long-standing principle of English common law, from which most of U.S.
self-defense law is derived, is that one has a “duty to retreat” before using lethal force
against an assailant. The exception to this principle is when one is threatened by an
intruder in one’s own home, as the home is one’s “castle”. In 2005, Florida became the
first in a recent wave of states to extend castle doctrine to places outside the home, and to
expand self-defense protections in other ways. Since then, more than 20 states have
followed in strengthening their self-defense laws by passing versions of “castle doctrine” or
“stand-your-ground” laws. While the specific components vary across states, the laws
eliminate the duty to retreat from a list of specified places, and often remove civil liability
for those acting under the law and establish a presumption of reasonableness as to the
beliefs and actions of the individual claiming self-defense. For ease of exposition, we
subsequently refer to these laws as castle doctrine laws.
These laws alter incentives in two important ways. First, the laws reduce the
expected cost of using lethal force. Castle doctrine lowers the expected legal costs
associated with defending oneself against criminal and civil prosecution, as well as the
probability that one is ultimately found criminally or civilly liable for the death or injury
inflicted. In addition, the laws increase the expected cost of committing violent crime, as
victims are more likely to respond by using lethal force. The purpose of our paper is to
examine empirically whether people respond to these incentives, and thus whether the laws
lead to an increase in homicide, or to deterrence of crime more generally.
In doing so, our paper also informs a vigorous policy debate over these laws.
2
Proponents argue these statutes provide law-abiding citizens with additional necessary
protections from civil and criminal liability. They argue that since the decision to use
lethal force is a split-second one that is made under significant stress, the threatened
individual should be given additional legal leeway. Critics argue that existing self-defense
law is sufficient to protect law-abiding citizens, and extending their scope will
unnecessarily escalate violence. These potential consequences have been of particular
interest recently following some highly publicized cases.1 In examining the empirical
consequences of these laws, this study informs the debate over their costs and benefits.
We use state-level crime data from 2000 to 2010 from the FBI Uniform Crime
Reports to empirically analyze the effects of castle doctrine on two types of outcomes.
First, we examine whether these laws deter crimes such as burglary, robbery, and
aggravated assault. In doing so, we join a much larger literature on criminal deterrence
generally (e.g., Becker, 1968; Ehrlich, 1973; Di Tella and Schargrodsky, 2004; Donohue and
Wolfers, 2009). More specifically, however, we join a smaller literature focused on
whether unobserved victim precaution can deter crime. For example, Ayres and Levitt
(1998) examine whether LoJack reduces overall motor vehicle thefts, while others have
examined whether laws that make it easier to carry concealed weapons deter crime
(Bronars and Lott, 1998; Dezhbakhsh and Rubin, 1998; Lott and Mustard, 1997; Ludwig,
1998).2
1 The most publicized case is that of Trayvon Martin, an unarmed teenager who was shot and killed by a neighborhood watch volunteer (Alvarez, 2012). 2 Our view is that relative to shall-issue concealed carry laws, the potential for castle doctrine law to deter crimes is quite large. For example, in Texas only 1.5 percent of adults age 18 and older have a concealed carry permit, and presumably only a fraction of those carry a gun on a regular basis (Texas Department of Public Safety, 2006; Texas Department of State Health Services, 2006; and author’s calculations). In contrast, Gallup polls indicate that from 2000 to 2009, 44 percent of households own a gun that could be used in self-defense against a burglar or assailant (Saad, 2011). Moreover, strengthened self-defense laws lower the cost of using a concealed carry weapon.
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We then examine whether lowering the expected cost of using lethal force results
in an increase in homicide, defined as the sum of murder and non-negligent manslaughter.
We also examine the effects of the laws on other outcomes in order to shed light on why
homicides are affected by the laws.
To distinguish the effect of the laws from confounding factors, we exploit the
within-state variation in the adoption of laws to apply a difference-in-differences
identification strategy. Intuitively, we compare the within-state changes in outcomes of
states that adopted laws to the within-state changes in non-adopting states over the same
time period. Moreover, we primarily identify effects by comparing changes in castle
doctrine states to other states in the same region of the country by including Census
region-by-year fixed effects. Thus, the crucial identifying assumption is that in the
absence of the castle doctrine laws, adopting states would have experienced changes in
crime similar to non-adopting states in the same region of the country. Our data allow us
to test and relax this assumption in several ways. First, we empirically test whether
outcomes in the two groups of states diverge in the year prior to adoption. In addition, we
show that our findings are robust to the inclusion of time-varying covariates such as
demographics, policing, economic conditions, and public assistance, as well as to the
inclusion of contemporaneous crime levels unaffected by castle doctrine laws that proxy for
general crime trends. Along similar lines, we offer placebo tests by showing that castle
doctrine laws do not affect crimes that ought not be deterred by the laws, such as vehicle
theft and larceny. Failing to find effects provides further evidence that general crime
trends were similar in adopting and non-adopting states. Finally, we allow for
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state-specific linear time trends.
Results indicate that the prospect of facing additional self-defense does not deter
crime. Specifically, we find no evidence of deterrence effects on burglary, robbery, or
aggravated assault. Moreover, our estimates are sufficiently precise as to rule out
meaningful deterrence effects.
In contrast, we find significant evidence that the laws lead to more homicides.
Estimates indicate that the laws increase homicides by a statistically significant 8 percent,
which translates into an additional 600 homicides per year across states that adopted castle
doctrine. The magnitude of this finding is similar to that reported in a new paper by
McClellan and Tekin (2012), who examine these laws’ effect on firearm-related homicide
using death certificate data from Vital Statistics.3,4 We further show that this divergence in
homicide rates at the time of castle doctrine enactment is larger than any divergence
between the same groups of states at any time in the last 40 years, and that magnitudes of
this size arise rarely by chance when randomly assigning placebo laws in
similarly-structured data sets covering the years prior to castle doctrine. In short, we find
compelling evidence that by lowering the expected costs associated with using lethal force,
castle doctrine laws induce more of it.
This increase in homicides could be due either to the increased use of lethal force
3 One advantage of using FBI UCR data is that it allows us to assess both how the laws affect the use of lethal force and whether they deter violent crime. In addition, the nature of the UCR data enables us to measure all homicides, rather than just those caused by firearms. The data also allow us to examine homicide subclassifications and relative changes in reported justifiable homicide from the SHR, along with assumptions about the degree of underreporting, to address the issue of whether the additional homicides are legally justified. This is not possible using data originating from death certificates. The primary disadvantage of the UCR homicide data is that while the annual state-level data we use are regarded as accurate and there is no reason to believe that any total homicide reporting issue at any level should be systematically correlated with changes in castle doctrine law, the monthly data from Vital Statistics are more complete. 4 Our findings contrast with those of Lott (2010) in More Guns, Less Crime, who reports that castle doctrine laws adopted from 1977 through 2005 reduced murder rates and violent crime.
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in self-defense situations that are classified by police as murder, or to the escalation of
violence in otherwise non-lethal conflicts. In an attempt to shed light on whether the
increase is driven entirely by legally justified homicide, we look at two things. For
evidence of escalation by criminals, we look at whether criminals are more likely to carry a
gun while committing robbery, and whether there is an increase in felony-type and
suspected felony-type murder, in which a murder is committed during the commission of
another felony. There is little robust evidence of an increase in criminals’ propensity to
carry a weapon. Estimated effects on felony-type and suspected felony-type murder are
positive and large, but imprecisely estimated.
In addition, we look at justifiable homicide, which is a separate classification
available in the Supplemental Homicide Reports. One concern with these data is
underreporting; Kleck (1988) estimates that only one-fifth of legally justified homicides are
classified that way by police. Nevertheless, we would still expect relative changes in
reported justifiable homicides before and after the laws to be informative, and show that
while our best back-of-the-envelope estimate is that roughly half of the additional
homicides caused by castle doctrine are legally justified, stronger assumptions about the
degree of underreporting (e.g., one-tenth compared to one-fifth) can lead one to conclude
that all of the additional homicides caused by castle doctrine are legally justified. We
emphasize that any conclusion depends on assumptions regarding the degree and nature of
underreporting of justifiable homicide by police.5
Collectively, these findings suggest that incentives do matter in one important
5 Of course, there is also the issue of whether all legally justified homicides under castle doctrine are socially desirable, which is beyond the scope of this paper.
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sense: lowering the threshold for the justified use of lethal force results in more of it. On
the other hand, there is also a limit to the power of incentives, as criminals are apparently
not deterred by empowering victims to use lethal force to protect themselves.
These findings also have significant policy implications. The first is that these
laws do not appear to offer any hidden spillover benefits to society at large. Rather, the
evidence indicates that the benefits of strengthening self-defense laws begin and end with
the added protections to actual victims of violent crime. On the other hand, the primary
potential downside of the law is the increased number of homicides. Thus, our view is
that any evaluation of these laws ought to weigh the benefits of increased self-defense
against the net increase in loss of life caused by the laws.
2. Castle Doctrine Law and Identification
2.1 Castle Doctrine Law
U.S. self-defense law, which stems from English common law, has long favored
the principle of “retreat to the wall”, which means that only after no longer being able to
retreat safely could one respond to an attacker with deadly force (Vilos and Vilos, 2010).
The exception to this rule is if the attack is inside one’s home, or “castle”, in which case
there is no longer a duty to retreat. In 2005, a wave of states began removing the duty to
retreat from places outside the home, as well as strengthening self-defense laws in several
other ways. For example, most laws added language that explicitly states individuals are
justified in using deadly force in certain circumstances when they reasonably believe that
they face a serious risk of imminent death or serious bodily harm. In addition, castle
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doctrine laws removed the duty to retreat in a list of special places such as one’s vehicle,
place of work and, in some cases, any place one has a legal right to be. Additionally,
many of these laws also added a presumption of reasonable fear of imminent serious injury
or death, which shifts the burden of proof to the prosecutor to show someone acted
unreasonably.6 Similarly, many laws also grant immunity from civil liability when using
defensive force in a way justified under law. Collectively, these laws lower the cost of
using lethal force to protect oneself, though they also lower the cost of escalating violence
in other conflicts.
Our understanding is that the main rationale for these laws was to provide
additional legal leeway to potential victims in self-defense situations, not to deter crime.
Thus, there is little reason to believe that the enactment of these laws coincided with either
other policies expected to affect crime or homicides, or with expectations about future
crime.7
To determine if and when states passed castle doctrine laws, we searched news
releases and other sources such as the Institute for Legislative Action of the National Rifle
Association to determine whether a state appeared to have passed a law that strengthened
self-defense law these ways. Specifically, we coded the specific attributes of each state
statute found, and classified whether the law i) removed the duty to retreat from somewhere
outside the home, ii) removed the duty to retreat from any place one has a legal right to be,
6 For example, the law passed in Florida states that “a person is presumed to have held a reasonable fear of imminent peril of death or bodily injury to himself or herself or another when using defensive force that is intended or likely to cause death or bodily injury to another.” 7 The National Rifle Association (NRA) was a major proponent of these laws (Goode, 2012). We are unaware of any statement by the NRA that suggests their support for the laws is due to a belief that the law will deter crime, or that the law is a necessary response to recent changes in violent crime. Rather, our understanding is that supporters view castle doctrine as an issue of individual and victim rights.
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iii) added a presumption of reasonable fear for the person using lethal force, and iv)
removed civil liability for those acting under the law. We then classified a state as having
a castle doctrine law if they remove the duty to retreat in some place outside the home.
Our goal in doing so was to create a list of states that extended castle doctrine and generally
passed meaningful changes to their self-defense law that would be widely reported.8
Table 1 shows the list of states classified as those enacting castle doctrine between
2000 and 2010. We classify 21 states as having passed castle doctrine laws, as each of
these states extended the castle doctrine to places outside the home.9 Of these, 17 states
removed the duty to retreat in any place one has the legal right to be, 13 included a
presumption of reasonable fear, and 18 explicitly removed civil liability. Our main
analysis groups all of these laws together, and thus captures the average effect of passing a
law similar to those passed in these 21 states. However, since that approach is perhaps
unnecessarily blunt, in appendix Table A1 we show results from different subgroups and
find that the results are largely similar to the average effects. We note, however, that due
to the high degree of collinearity and the potential for interaction effects, distinguishing
between effects caused by different attributes of these laws is difficult.
8 We are aware of four states that passed laws removing civil liability that that made no other changes to self-defense law over this time period, including Idaho (2006), Maryland (2010), Maine (2007), and Illinois (2004). We do not code those states as castle doctrine states. We also do not classify Wyoming as having passed a castle doctrine law, though we note that they removed civil liability and added a presumption of reasonable fear (provisions that removed the duty to retreat were stripped out prior to passage) (Vilos and Vilos, 2010). Finally, we note that in an earlier version of the paper we defined these laws somewhat more broadly, and somehow missed that Oklahoma passed a castle doctrine law on November 1st of 2006. We thank McClellan and Tekin (2012) for helpful conversations about the specific attributes of laws passed in different states. 9 To avoid confusion over which states are driving the within-state variation used in our study, we intentionally leave states off Table 1 if they had passed a law that extended castle doctrine prior to 2000 or after 2010, which are outside our sample period.
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2.2 Crime Data
Outcome data come from the FBI Uniform Crime Reports (UCR) and cover all 50
states from 2000 – 2010. 10 Specifically, we use homicide, burglary, robbery, and
aggravated assault data from the official UCR data published online by the FBI.11 In
addition, for the other variables not available from the online UCR, we use data from the
FBI’s Master files (Return A and Supplemental Homicide Report).
We use these data to test whether making it easier for individuals to use lethal
force in self-defense deters crime or increases homicide. For deterrence, we focus on
three criminal outcomes. The first is burglary, which is defined as “the unlawful entry of
a structure to commit a felony or a theft” (FBI, 2004). The second is robbery, defined as
“the taking or attempting to take anything of value from the care, custody, or control of a
person or persons by force or threat of force or violence and/or by putting the victim in fear”
(FBI, 2004). Finally, we also examine aggravated assault, which the FBI defines as “an
unlawful attack by one person upon another for the purpose of inflicting severe or
aggravated bodily injury”, and is typically accompanied by the use of a weapon (FBI,
2004).12 In all cases, one might expect rational criminals to be less likely to commit such
crimes under castle doctrine, as the increased scope for the use of justifiable lethal force on
10 There are relatively few cases of missing data. Data on whether robbery was committed with a gun were missing from 2000 to 2005 for Illinois. Justifiable homicide data were missing for Florida, so we requested and received those data directly from the Florida Department of Law Enforcement Office. 11 These data include corrections by the FBI to adjust for under-reporting by police agencies. We note, however, that results are qualitatively and quantitatively similar if we instead use data from the Supplemental Homicide Report and Return A from the FBI Master files, which were acquired directly from the FBI and include statistics reported after the deadline, but do not correct for under-reporting. For example, estimates corresponding to the homicide estimates in the 6 columns of Panel A in Table 5 are 0.0853, 0.0926, 0.0850, 0.0892, 0.0729, and 0.128, respectively. All are significant at the 1 percent level. 12 Results are similar using data on all assaults, including simple assault, which were obtained from Return A of the FBI Master files.
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the part of the victim raises the expected cost to the criminal.13
Our last set of outcomes is intended to measure the escalation of violence. The
primary outcome we examine is total homicides, which is defined as the sum of murder and
non-negligent manslaughter, although we also look at murder separately to determine
exactly how police are classifying the additional homicides.14
An increase in homicide could represent either an increase in legally justified
homicide that is reported as murder or non-negligent manslaughter, or the escalation of
violence by criminals, or the escalation of violence in otherwise non-violent situations.15
In order to shed light on that issue, we look at two other outcomes, both of which measure
the escalation of violence by criminals in response to castle doctrine. The ratio of
robberies committed with a gun measures whether criminals respond by being more likely
to carry and use weapons during the commission of a crime, as one might expect if they
believe they will be faced with lethal force by the victim. We also look at felony-type and
suspected felony-type murders, which also measure the escalation of violence by criminals.
We expect to see increases in these outcomes if castle doctrine laws induce criminals to be
more likely to carry and use deadly weapons during the commission of crimes.
In addition, we also ask whether the laws increase homicides that are reported
to the FBI as “justifiable homicides by private citizens”, which the FBI defines as “the
killing of a felon during the commission of a felony” (Uniform Crime Reporting Handbook,
13 To the extent castle doctrine increases homicide, however, the hierarchy rule means that our results are (slightly) biased in favor of finding deterrence effects. The hierarchy rule instructs reporting agencies to only code the highest, or most serious, offense in multiple-offense situations. Thus, a burglary that escalates into a homicide due to castle doctrine will be coded as a homicide, which would lead us to (slightly) overstate the magnitude of deterrence. 14 Homicide figures come from the UCR data published online and do not include justifiable homicides. Murder figures come from the FBI’s Return A, since murder is not available as a separate category in the published UCR. 15 The general possibility that disputes can escalate dramatically in environments perceived to be dangerous is discussed in O’Flaherty and Sethi (2010).
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2004). The major disadvantage of these data is that they are widely believed to be
underreported; Kleck (1988) estimates that around one-fifth of legally justified homicides
are reported that way to the FBI. However, note that we use these data only to look for
evidence of relative changes in legally justified homicide, and then use those estimates
along with varying assumptions about the degree of underreporting in order to determine if
the entire increase in homicides can be explained by legally justified homicide. What
would complicate our analysis is if reporting changed over time in a way that was
systematically correlated with the passage of castle doctrine. While the FBI Uniform
Crime Reporting Handbook makes it look to us that this should not be the case,16 our view
is that if anything, police agencies are probably more likely to report homicides as
justifiable after the passage of these laws. Thus, we interpret our estimates as upper
bounds on the relative effect of castle doctrine on legally justifiable homicide.
The data also allow us to perform several placebo, or falsification tests. For
example, because the focus of castle doctrine laws is on civilians, and not law enforcement,
we examine whether we detect effects of the laws on justifiable homicide by police.
Similarly, we use data on the rate of larceny and motor vehicle theft to determine whether
16 For example, the handbook emphasizes that “law enforcement agencies must report the willful (non-negligent) killing of one individual by another, not the criminal liability of the person or persons involved” (Uniform Crime Reporting Handbook, 2004). In addition, the handbook emphasizes that by definition, justifiable homicide occurs in conjunction with other offenses, and those other offenses must be reported. The handbook explicitly states that “reporting agencies should take care to ensure that they do not classify a killing as justifiable or excusable solely on the claims of self-defense or on the action of a coroner, prosecutor, grand jury, or court” (Uniform Crime Reporting Handbook, 2004). Additionally, the handbook gives examples of specific hypothetical events that would and would not qualify as justifiable homicide under the guidelines. An example given of an incident that would qualify as a justifiable homicide is “When a gunman entered a store and attempted to rob the proprietor, the storekeeper shot and killed the felon” (Uniform Crime Reporting Handbook, 2004). Note that in the absence of castle doctrine law, this may not qualify as a self-defense case (though it could, of course), but according to the guidelines should still have been reported as a justifiable homicide. An example of what would NOT qualify as a justifiable homicide is “While playing cards, two men got into an argument. The first man attacked the second with a broken bottle. The second man pulled a gun and killed his attacker. The police arrested the shooter; he claimed self-defense” (Uniform Crime Reporting Handbook, 2004). Note here that under castle doctrine, the shooter may well have been justified as acting in self-defense, though again the reporting handbook explicitly states that this would not qualify as a justifiable homicide under the guidelines.
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castle doctrine laws appear to affect those crimes.17 In both cases we expect to find no
effects so long as the identifying assumptions of our difference-in-difference research
design hold, which we discuss at length in the next section.
Finally, we have data on several time-varying control variables. Specifically, we
have measures of the number of full-time equivalent police per 100,000 state residents
(Uniform Crime Reports, 2000-2010). We also include both contemporaneous and lagged
measures of the number of persons incarcerated in state prison per 100,000 residents
(Bureau of Justice Statistics Bulletin, 2000-2010). These variables capture the effects of
deterrence and incapacitation caused by additional policing or incarceration. In addition,
we have two variables from the American Community Survey of the U.S. Census Bureau
that measure local legal opportunities, including median family income and the poverty rate.
We also have data on the share of white and black men in the 15-24 and 25-44 age groups
for each state over time (American Community Survey, 2000-2010). Finally, we measure
the generosity of public assistance in each state by measuring per capita spending on
assistance and subsidies and per capita spending on public welfare (US Census, 2000 –
2010).
3. Identification
To distinguish the effect of the castle doctrine laws from confounding factors, we
exploit the within-state variation induced by the fact that 21 states passed such laws
between 2000 and 2010. Specifically, we use a difference-in-differences research design
17 While it may be possible for castle doctrine law to deter these crimes as well, our view is that deterrence should be considerably less likely for these crimes than for burglary, robbery, and aggravated assault.
13
that asks whether outcomes change more in states that adopt castle doctrine laws than in
states that do not, and focus primarily on within-region comparisons.
Formally, we estimate fixed effects ordinary least squares (OLS) panel data
models, where we typically use the log of the outcome per 100,000 population as the
dependent variable. In addition, because a significant number of states report zero
justifiable homicides in a given year, we estimate a fixed effects negative binomial model
for that outcome as well as OLS models that use the number of justifiable homicides as the
dependent variable and control for population on the right-hand side. Ordinary least
squares models are estimated with and without weighting by state population. The OLS
model estimated is
Outcome
where itCDL is the treatment variable that equals the proportion of year t in which state i has
an effective castle doctrine law, itX is the vector of control variables, and ic and tu control
for state and year fixed effects, respectively. In addition, in most models we also include
Census region-by-year fixed effects, to allow states in different regions of the country to
follow different trajectories and account for differential shocks by region over time.18
Note that for states that enacted the law partway through a year, we set CDL equal to the
proportion of the year in which the law was in effect. Robust standard errors are clustered
at the state level, though we also do additional exercises in the spirit of Bertrand, Duflo,
and Mullainathan (2004) to ensure standard errors are being estimated accurately, as well as
to perform inference using placebo estimates from simulated pre-castle doctrine data.
18 There are four Census Regions: West, Midwest, Northeast, and South.
14
This last approach of using distributions of placebo estimates to do inference is similar in
spirit to the permutation inference approach used in the synthetic control method by Abadie,
Diamond, and Hainmueller (2010).
Since we primarily rely on specifications that include state fixed effects and
region-by-year fixed effects, the identifying assumption is that in the absence of the castle
doctrine laws, adopting states would have experienced changes in crime similar to
non-adopting states in the same region of the country. Our data allow us to test and relax
this identifying assumption in several ways. First, we offer a formal statistical test of this
by including an indicator in equation (1) for the year prior to the passage of the laws.
That is, we ask whether states that pass the laws diverge even before they pass the laws. If
they do, it suggests that the identifying assumption of our research design is violated.
We also examine whether time-varying determinants of crime are orthogonal to the
within-state variation in castle doctrine laws. Under our identifying assumption, factors
such as economic conditions, welfare spending, and policing intensity should not change
more over time in adopting states than non-adopting states, as this would suggest that crime
in the two groups might have diverged even in the absence of treatment. Thus, we
examine whether adding these controls changes our estimates in a meaningful way. To the
extent that our difference-in-differences estimates remain unchanged, it provides some
assurance that our research design is reasonable.19
Along similar lines, we also show results from specifications that include
contemporaneous motor vehicle theft and larceny as controls. While it is possible that
19 The primary concern is not that observed determinants vary systematically over time—we can control for those variables directly—but that if they do, it may suggest that unobserved determinants also change systematically over time in the treatment and control groups.
15
castle doctrine laws could affect these crimes, we would expect any such effects to be
second-order and at most small in magnitude. Thus, we use these crime measures as
controls that pick up any differential trends in crime in adopting and non-adopting states.
We also perform falsification exercises using these crimes as outcomes to explicitly test
whether castle doctrine laws appear to affect crimes unrelated to self-defense. If our
identifying assumption holds, we would expect to see no effects on these crimes.
Finally, we allow for state-specific linear time trends, thereby allowing each state
to follow a different trend.
4. Results
4.1 Falsification Tests
One way to test the identifying assumption is to directly examine whether crimes
that ought not be affected by the laws—and thus proxy for general crime trends—appear to
be affected by the laws.20 Finding effects on crimes that ought to be exogenous to castle
doctrine law would invalidate our research design.
Thus, we examine whether castle doctrine laws appear to affect larceny or motor
vehicle theft. While it is possible that these outcomes are affected directly by self-defense
laws, we argue that such effects should be second-order, at best.
Results are shown in Table 3, which uses a format similar to subsequent tables
showing other outcomes. Columns 1 through 6 represent OLS estimates that are weighted
by population, while Columns 7 through 12 are unweighted OLS estimates. The first
20 Similar tests are performed by Ayres and Levitt (1998), when they look for effects of Lojack on crimes other than motor vehicle theft.
16
column of each group controls for only state and year fixed effects. The second column
adds region-by-year fixed effects, while the third column adds time-varying controls. The
fourth column additionally includes an indicator variable for the year before the castle
doctrine law was adopted; the fifth drops the leading indicator but adds controls for
contemporaneous larceny and motor vehicle theft. Finally, the last column controls for
state fixed effects, region-by-year fixed effects, time-varying controls, and state-specific
linear time trends.
Estimates for larceny are close to zero and statistically insignificant across all
specifications. Estimates of the effect on the log of the motor vehicle theft rate are more
interesting. Results in columns 1 and 7 in which only state and year fixed effects are
included provide suggestive evidence of increases in motor vehicle theft of 5 to 8 percent,
the latter of which is significant at the 10 percent level. However, including
region-by-year fixed effects in columns 2 and 8 causes the estimate to drop to zero or even
turn negative, and both are statistically insignificant. This suggests that accounting for
differences in regional trends in some way may be important in assessing the impact of
castle doctrine laws.
4.2 Deterrence
We now examine whether strengthening self-defense law deters crime. We
examine three types of crime: burglary, robbery, and aggravated assault. To the extent that
criminals respond to the higher actual or perceived risk that victims will use lethal force to
protect themselves, we would expect these crimes to decline after the adoption of castle
17
doctrine.
Results are shown in Table 4, where the first 6 columns show estimates from an
OLS regression weighted by state population, while the second 6 columns are from
unweighted OLS regressions. Results in Column 1 in Panel A for burglary are similar to
the finding for motor vehicle theft, in that estimates range from 6 to 8 percent and are
statistically significant at the 5 percent level. Again, however, including region-by-year
effects in columns 2 and 8 reduces the estimates considerably, and all are statistically
indistinguishable from zero at the 5 percent level.
Importantly, there is little evidence of deterrence effects in any specification for any
outcome: of the 36 estimates reported, none are negative and statistically significant at the
10 percent level. The estimates are sufficiently precise as to rule out large deterrence
effects. For example, in our preferred specification in column 3, the lower bounds of
estimates on burglary, robbery, and aggravated assault are -2.1 percent, -1.9 percent, and
-2.5 percent. Put differently, our estimates and standard errors from column 3 indicate
that if we were to perform this castle doctrine policy experiment many times, we would
expect that 90 percent of the time we would find deterrence effects of less than 0.7 percent,
0.4 percent, and 0.5 percent for burglary, robbery, and aggravated assault, respectively. In
short, these estimates provide strong evidence against the possibility that castle doctrine
laws cause economically meaningful deterrence effects. Thus, while castle doctrine law
may well have benefits to those protecting themselves in self-defense, there is no evidence
that the law provides positive spillovers by deterring crime more generally.
18
4.3 Homicide
We now turn to whether strengthening self-defense laws increases homicide.
Given that the laws reduce the expected costs associated with using violence, economic
theory would predict that there would be more of it. This could be driven by any of
several possibilities. For example, the increase could be due to additional legally justified
killings that are not reported that way by police. Perhaps more troubling is the possibility
that under castle doctrine, conflicts or crimes that might not have otherwise turned deadly
now do. For example, a criminal may not have intended to kill someone he was robbing
until the victim attempted to use a weapon in self-defense. Alternatively, criminals may
escalate violence in response to castle doctrine laws and cause in increase in homicide.
Results are shown in Panels A, B, and C of Table 5, which show
population-weighted OLS estimates, unweighted OLS estimates, and unweighted estimates
from a negative binomial model. Estimates from the negative binomial regression are
interpreted in the same way as those from a log-linear OLS model. Results from the
population-weighted shown in Panel A indicate that the homicide rate is increased by 8 to
10 percent; all 6 estimates are statistically significant at the 5 percent level, and 3 are
significant at the 1 percent level. Estimates from unweighted OLS regressions shown in
Panel B range from 5 to 9 percent, though all are measured imprecisely: t-statistics range
from 0.6 to 1.5. Estimates in Panel C from a negative binomial model indicate castle
doctrine leads to a 6 to 11 percent increase in homicide. All negative binomial estimates
that include region-by-year fixed effects are significant at the 5 percent level, and that
which does not (column 1) is significant at the 10 percent level.
19
We have also done additional tests in order to ensure that we are making correct
inferences about statistical significance. Toward that end, we do tests in the spirit of
Bertrand et al. (2004), in which we randomly select 11-year panels from 1960 to 2004, and
then randomly assign states to the treatment dates found in our data, without replacement.
Thus, we assume that one state adopted castle doctrine on October 1st of the 6th year of the
11-year panel (just as Florida actually adopted in 2005, the 6th year of our panel), and that
13 more states adopted in the 7th year of the 11-year panel, etc. We generate distributions
of estimates, and ask how often we reject the null hypothesis of no effect at the 5 percent
level, as well as what proportion of the placebo estimates are larger than the actual
estimated effect of (real) castle doctrine. The latter figure corresponds to a p-value and is
similar to the method used in synthetic control methods (Abadie et al., 2010), as well as by
Chetty, Looney, and Kroft (2009).
The resulting placebo distributions are shown in Figures 1, 2, and 3, and
correspond to Table 5 results from column 2 of Panels A, B, and C, respectively. Results
from population-weighted OLS placebo estimates suggest that robust clustered standard
errors may be a bit too small: 11.9 percent of simulated estimates are significant at the 5
percent level. However, the estimate of 9.46 percent in column 2 ranks in the 95th
percentile of placebo estimates, which means only 5 percent of placebo estimates are larger
than it is.
Results for unweighted OLS simulation results are also interesting. On the one
hand, simulations corresponding to the specification in column 2 of Panel B in Table 5
suggest that clustered standard errors from unweighted OLS regressions are accurate: 6.3
20
percent of the simulated estimates are significant at the 5 percent level. At the same time,
however, the estimate of 8.1 percent shown in Table 5 corresponds to the 94.7th percentile
(see Figure 2), which would give it a p-value of 5.3 percent using the Abadie et al. (2010)
approach to inference. This suggests that results in Panel B of Table 5 understate the
degree of statistical significance.
Finally, simulations for the fixed effect negative binomial model corresponding to
column 2 in Panel C indicate that 9.7 percent of placebo estimates are significant at the 5
percent level, while 15.6 percent are significant at the 10 percent level. As shown in
Figure 3, the estimate of 7.3 percent in Table 5 ranks at the 95.4th percentile, as fewer than
5 percent of placebo estimates were larger than the actual estimate in the simulations.
On the basis of these exercises, we conclude that it is unlikely that we would have
obtained estimates of the magnitude and statistical significance shown in Panels A, B, and
C of Table 5 due to chance.
We have also performed simulations to see if the homicide rates of these particular
21 states ever diverged in the way they did after adopting castle doctrine in the late 2000s.
To do so, we created 40 panel data sets, each covering separate 11-year time periods
between 1960 and 2009. In each 11-year panel, we assume that Florida adopts castle
doctrine on October 1st of the 6th year, and that the 13 states that adopted in 2006 adopted in
the 7th year, etc. None of the 40 estimates corresponding to either the OLS
population-weighted regressions or from the negative binomial regression were larger than
those shown in column 2 of Table 5. In the case of the OLS unweighted regressions, only
1 of the 40 placebo estimates was larger than the actual estimate of 8.1 percent shown in
21
Column 2, Panel B, of Table 5.21 Thus, there is no evidence that the homicide rates in
castle doctrine states show a general tendency to diverge from their regional counterparts:
in the last 40 years they have almost never done so as much as they did immediately after
they passed castle doctrine.
The relative increase in the homicide rate can be seen graphically as well. Figure
4 shows the log of homicide rate for the 13 states that adopted castle doctrine in 2006, as
well as for the 29 states that did not extend castle doctrine from 2000 to 2010.22 It shows
that while the trends of the two groups track each other closely prior to castle doctrine,
afterward homicides in adopting states increase relative to control states. Importantly,
Figure 1 also gives us little reason to believe that even in the absence of castle doctrine,
adopting states would have experienced an increase in homicides after 2005 relative to
non-adopting states.23
Collectively, we view these findings as compelling evidence that castle doctrine
increases homicide. However, we note that one downside of the homicide measure is that
it could well include homicides that are justified under the new self-defense law, but are not
reported separately as justifiable homicides. Thus, this increase may not be viewed by
everyone as unambiguously bad. For example, the increase could be driven by
21 The one larger estimate was 10.5 percent, and was from the 1975 to 1985 time period. 22 It is more difficult to show a meaningful graph for the entire sample of adopting states, as they enacted castle doctrine in different years. Also, note that this graph does not rely on within-regional variation, unlike most of our specifications. Nevertheless, we think that showing results graphically for the largest subset of states that passed the law in the same year gives the reader a rough idea of the raw variation in the data. 23 As shown in Figure 1, adopting states have homicide rates that are about 30 percent higher than non-adopting states. However, because we are using a difference-in-differences research design that conditions on year and state fixed effects, differences in levels is not a concern for identification. Instead, what would worry us is if the homicide rate in adopting states increased more than in non-adopting states even before treatment, as that would suggest that the groups might have continued to diverge afterward, regardless of castle doctrine. We see no evidence of that, which suggests that the relative increase seen after 2005 is caused by castle doctrine. Moreover, note that homicide estimates remained similar even after controlling for time-varying police and incarceration rates and other controls, including region-by-year fixed effects, and allowing for state-specific linear time trends.
22
individuals protecting themselves from imminent harm by using lethal force. On the other
hand, the increase could be driven by the escalation of violence in situations that otherwise
would not have ended in serious injury for either party. Note, however, that the net
increase cannot be driven by a one-to-one substitution of homicides of assailants for
homicides of innocent victims. In contrast, in order for the entire increase in homicide to
be driven by life-saving use of force, there would have to be at least some cases of multiple
killed assailants by a would-be-killed victim.
Results in Panel D of Table 5 show results for murder, which excludes
non-negligent manslaughter classifications that one might think would be used more often
in self-defense killings not classified as justifiable homicides. Estimates indicate a
similarly sized increase in murder, which suggests that police are largely classifying these
additional homicides as murders.
Given the robustness of the estimates to various specifications, it is worth
considering what one would have to believe for a confounding factor to cause the observed
increase in murder/homicide, rather than castle doctrine. That is, one would have to
believe that something else caused homicides to increase relative to non-adopting states
immediately after castle doctrine was enacted, but not in the years prior to enactment. In
addition, this confounder must have only caused a divergence in homicide rates in the late
2000s coincidental with the passage of castle doctrine, and not at any point in the 40 years
prior. Furthermore, this confounder must cause an increase in homicides in castle doctrine
states after adoption, but not cause a similar increase in states in the same region of the
country that did not adopt castle doctrine at that time. Additionally, the confounder must
23
cause adopting states to diverge from their own pre-adoption trend in homicide rate,
coincidental with the enactment of castle doctrine. The confounder must also increase
homicides in adopting states after adoption without causing proportionate increases in
motor vehicle theft and larceny. Finally, the confounder must be uncorrelated with
changes in the economic conditions, welfare generosity, and the rates of incarceration and
policing in adopting states immediately following adoption. We are unable to think of any
confounding factor that would fit this description, and thus we interpret the increase in
homicides as the causal effect of castle doctrine.
4.4 Homicide: Interpretation
While it is clear that the increase in homicide is largely being classified by police
as murder, it is possible that this represents a misclassification by police. Here we look
directly for evidence of this and other interpretations. We start by assessing whether
criminals appear to escalate violence in response to castle doctrine laws. For example, a
rational criminal may respond to a real or perceived increase in the likelihood of
encountering a victim willing to use lethal force by using a deadly weapon himself. Thus,
we examine whether castle doctrine increases felony-type and suspected felony-type
murders, which appeared to be committed during a felony. Results are shown in Panel A
of Table 6. The estimate from column 1, which controls only for state and year fixed
effects, is 10 percent and is statistically indistinguishable from zero. Estimates from
specifications including region-by-year fixed effects are more suggestive of a criminal
escalation effect: estimates in columns 2 through 5 are around 20 percent and are
24
statistically significant at the 10 percent level, though we note the estimate goes to zero
when allowing for state-specific time trends in column 6. We also examine whether
criminals are more likely to use guns during robberies.24 Results in Panel B of Table 6
indicate that there is little evidence of this type of escalation, at least once one compares
states to others in their same region.25 In short, while we find some suggestive evidence
of escalation by criminals, the evidence is far from conclusive.
Finally, we turn to evidence on whether the laws increase the reported number of
justifiable homicides. The problem with these data is that justifiable homicides are
believed to be underreported: Kleck (1988) estimates that only one-fifth of legally justified
homicides by civilians are reported. However, even though the level of justifiable
homicides may be underreported, relative changes in justifiable homicide may still be
informative. As a result, we focus on examining the relative increase in reported
justifiable homicide, and then estimate how many additional legally justified homicides
there really are by scaling the pre-castle doctrine figure by estimates of underreporting.
Results are shown in Panels C, D, and E of Table 6. Panel C shows estimates
from population-weighted regressions in which the number of justifiable homicides is the
dependent variable. Results range from 1 to 9 additional justifiable homicides, which is
relative to a baseline population-weighted average of 10.0 justifiable homicides per state in
the year prior to castle doctrine enactment. Thus, the estimate when controlling for only
24 We also look at the proportion of assaults in which a gun was used and find no evidence of an increase, though the baseline rate is small (3 percent). We also note that examining these ratios as outcome variables could be problematic if the laws were found to reduce robbery or aggravated assault. However, as we show in Table 4 there is no effect on robberies or aggravated assaults. 25 It is difficult to think of how using other FBI classifications could help answer this question. For example, the FBI classifies some non-felony-type homicides as having originated in an argument. It is difficult to know, however, whether the argument would have resulted in serious injury to the killer, had that person not used lethal force, or if the argument escalated from, say, a fistfight into a homicide. Yet most would agree that the latter is more disturbing than the former.
25
state and year fixed effects suggests that there is a statistically significant 90 percent
increase in justifiable homicide as a result of the law, though we note that the estimate in
our preferred specification in column 3 is 4.6, is statistically significant at the 10 percent
level, and represents a 46 percent increase.26
Panel D shows estimates from unweighted regressions of similar form. Results
range from 1 to 4.3 additional justifiable homicides per state per year as a result of castle
doctrine. The estimate in our preferred specification is 3.4, is statistically significant at
the 5 percent level, and represents a 70 percent increase over the pre-castle doctrine average
of 4.9 justifiable homicides per year.
Panel E reports estimates from a negative binomial model. Estimates range from
an insignificant 22 percent increase to a significant 57 percent increase in column 1, which
does not control for region-by-year fixed effects.
Using these estimates, we now turn to assessing whether the relative increases
observed in Table 6 can explain the entire increase in homicide, given estimates of the
degree of underreporting of legally justified homicide. The largest estimated relative
increase from a specification in Table 6 that controls for region-by-year fixed effects is 70
percent, which is relative to a baseline total of 103 justifiable homicides across the 21 states
in the year prior to castle doctrine enactment. We assume that i) police departments are
not less likely to report an otherwise-identical homicide as justifiable after castle doctrine,
and ii) the relative increase in legally justified homicide due to castle doctrine is no lower
for reporting agencies than for non-reporting agencies. We view the first of these
26 In contrast, we find no evidence of an increase in justifiable homicide by police, consistent with the identifying assumption. Results are shown in Table A2 of the web appendix.
26
assumptions as likely to hold, and the second as reasonable, though we emphasize that they
are in fact assumptions. Combining these assumptions with our estimates in Table 5
suggests that the true castle-doctrine-induced relative increase in legally justified homicide
across the 21 states should be no larger than 70 percent.
Kleck (1988) reports that approximately one-fifth of legally justified homicides are
reported correctly, while the others are classified as homicides. Given the 103 reported
pre-castle doctrine justifiable homicides, that suggests that the true figure is 515. A 70
percent increase means that castle doctrine causes an additional 361 legally justified
homicides, of which 289 (80 percent) would be (mis)reported as homicides. Recall that
estimates from Table 5 indicate that castle doctrine causes approximately an 8 percent
increase in homicide, which translates to an additional 611 homicides given the 7,632
pre-castle doctrine homicides. Thus, under these assumptions, our best guess is that
around half of the additional homicides caused by castle doctrine were legally justified.
Of course, different assumptions yield different conclusions. For example,
assuming that only 10 percent of legally justified homicides are reported correctly, along
with a 70 percent relative increase and the second assumption outlined above, would
suggest that all of the additional homicides were legally justified.
In summary, we find no evidence that strengthening self-defense law deters crime.
On the other hand, we find that a primary consequence of castle doctrine laws is to increase
homicide by a statistically and economically significant 7 to 10 percent. Relative
increases in justifiable homicide along with Kleck’s (1988) estimate of the degree of
underreporting suggest that it is unlikely, but not impossible, for the increase in homicides
27
to consist entirely of legally justified homicides. We emphasize, however, that one’s
conclusion on that issue depends on assumptions about the nature and degree of
underreporting of legally justified homicides.
5. Conclusion
In recent years, more than 20 states have strengthened their self-defense laws by
adopting castle doctrine laws. These statutes widen the scope for the justified use of lethal
force in self-defense by stating the circumstances under which self-defense is justified and
removing the duty to retreat from a list of protected places outside the home. In addition,
in some cases they establish a presumption of reasonableness and remove civil liability.
Thus, these laws could hypothetically deter crime or, alternatively, increase homicide.
Results presented indicate that castle doctrine law does not deter crime.
Furthermore, our estimates are sufficiently precise as to rule out moderate-sized deterrence
effects. Thus, while our view is that it is a priori reasonable to expect that strengthening
self-defense law would deter crime, we find this is not the case.
More significantly, results indicate that castle doctrine laws increase total
homicides by around 8 percent. Put differently, the laws induce an additional 600
homicides per year across the 21 states in our sample that enacted castle doctrine laws.
This finding is robust to a wide set of difference-in-differences specifications, including
region-by-year fixed effects, state-specific linear time trends, and controls for time-varying
factors such as economic conditions, state welfare spending, and policing and incarceration
rates. These findings provide evidence that lowering the expected cost of lethal force
28
causes there to be more of it.
A critical question is whether all of the additional homicides that occur were
legally justified. Using results on the effect of the laws on justifiable homicide, along
with assumptions about the degree to which justifiable homicides are underreported, we
report back-of-the-envelope calculations that make it difficult to explain the entire increase
in homicide with an increase in legally justified homicide. Our view is that this provides
suggestive evidence that at least some of the additional homicides were not legally justified,
though we emphasize that conclusions on this issue depend on assumptions regarding the
degree and nature of the underreporting of justifiable homicide by police to the FBI.
With respect to policy, our findings suggest that an informed debate over these
laws will weigh the increased protections given to victims against the net increase in
violent deaths that result. More broadly, our findings indicate that incentives and expected
costs matter when it comes to the decision of whether to use lethal force.
29
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32
Figure 1: Empirical Distribution of Placebo Homicide Estimates: Population-Weighted OLS
Notes: The vertical line represents the actual estimated effect of castle doctrine on log homicide of 0.0946, as shown in Column 2, Panel A, Table 5. A total of 5.0 percent of placebo estimates lie to the right of this estimate. Figure 2: Empirical Distribution of Placebo Homicide Estimates: Unweighted OLS
Notes: The vertical line represents the actual estimated effect of castle doctrine on log homicide of 0.0811, as shown in Column 2, Panel B, Table 5. A total of 5.3 percent of placebo estimates lie to the right of this estimate.
33
Figure 3: Empirical Distribution of Placebo Homicide Estimates: Negative Binomial
Notes: The vertical line represents the actual estimated effect of castle doctrine on homicide of 0.0734, as shown in Column 2, Panel C, Table 5. A total of 4.6 percent of placebo estimates lie to the right of this estimate. Figure 4: Log Homicide Rate for the 13 States That Enacted Castle Doctrine in 2006 Compared to States That Did Not Enact Castle Doctrine from 2000 to 2010
34
Table 1: States that Extended Castle Doctrine Between 2000 and 2010
Alabama 2006 Yes Yes No YesAlaska 2006 Yes No Yes YesArizona 2006 Yes Yes Yes YesFlorida 2005 Yes Yes Yes Yes
Georgia 2006 Yes Yes No YesIndiana 2006 Yes Yes No YesKansas 2006 Yes Yes No Yes
Kentucky 2006 Yes Yes Yes YesLouisiana 2006 Yes Yes Yes YesMichigan 2006 Yes Yes No Yes
Mississippi 2006 Yes Yes Yes YesMissouri 2007 Yes No No YesMontana 2009 Yes Yes Yes No
North Dakota 2007 Yes No Yes YesOhio 2008 Yes No Yes Yes
Oklahoma 2006 Yes Yes Yes YesSouth Carolina 2006 Yes Yes Yes YesSouth Dakota 2006 Yes Yes No NoTennessee 2007 Yes Yes Yes Yes
Texas 2007 Yes Yes Yes YesWest Virginia 2008 Yes Yes No No
StateEffective
Year
Removes duty to retreat in any place one has a
legal right to be
Presumption of reasonable fear
Removes civil
liability
Removes duty to retreat somewhere
outside home
35
Table 2: Descriptive Statistics
Dependent VariablesHomicides per 100,000 Population 4.8 5.5
(2.5) (1.9)Justifiable Homicide by Private Citizens (count) 5.1 11.8
(8.2) (12.9)Justifiable Homicide by Police (count) 8.0 23.4
(16.9) (34.3)Robberies per 100,000 Population 107.2 143.1
(59.6) (47.5)Aggravated Assault per 100,000 Population 267 296
(131) (114)Burglary per 100,000 Population 710 744
(240) (235)Larceny per 100,000 Population 2,334 2,328
(533) (532)Motor Theft per 100,000 Population 331 381
(178) (174)Proportion of Robberies in Which a Gun Was Used 0.35 0.37
(0.13) (0.13)Control VariablesPolice per 100,000 residents 315 336
(65) (66)Unemployment Rate (%) 5.49 5.93
(1.99) (2.10)Poverty Rate (%) 12.4 12.9
(3.0) (2.6)Median Household Income ($) 51,648 52,146
(7873) (6895)Prisoners per 100,000 residents 439 461
(169) (150)Government spending (assistance and subsidies) per capita 125 110
(56) (48)Government spending (public welfare) per capita 1,319 1,344
(391) (409)% Black Male Aged 15-24 2.60 0.97
(4.61) (2.11)% White Male Aged 15-24 10.77 4.36
(17.70) (7.69)% Black Male Aged 25-44 4.32 1.61
(7.71) (3.53)% White Male Aged 25-44 21.97 8.88
(36.40) (15.90)
Mean (Unweighted) Mean (Weighted by Population)
Notes: Each cell contains the mean with the standard deviation in parentheses. All variables have 550observations except for the proportion of assaults in which a gun was used (544) and the proportion ofrobberies in which a gun was used (544).
36
Table 3: Falsification Tests: The Effect of Castle Doctrine on Larceny and Motor Vehicle Theft
1 2 3 4 5 6 7 8 9 10 11 12Panel A: LarcenyCastle Doctrine Law 0.00300 -0.00660 -0.00910 -0.0149 -0.00401 -0.00284 0.00745 0.00145 -0.00188 -0.00445 -0.00361 -0.0137
(0.0161) (0.0147) (0.0139) (0.0156) (0.0128) (0.0180) (0.0227) (0.0205) (0.0210) (0.0226) (0.0201) (0.0228)
-0.0197** -0.0103(0.00975) (0.0114)
Observation 550 550 550 550 550 550 550 550 550 550 550 550Panel B: Motor Vehicle Theft
Castle Doctrine Law 0.0517 -0.0389 -0.0252 -0.0320 -0.0165 -0.00708 0.0767* 0.0138 0.00814 0.00775 0.00977 -0.00373(0.0563) (0.0448) (0.0396) (0.0451) (0.0354) (0.0372) (0.0413) (0.0444) (0.0407) (0.0462) (0.0391) (0.0361)
-0.0231 -0.00155(0.0233) (0.0287)
Observation 550 550 550 550 550 550 550 550 550 550 550 550State and Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesRegion-by-Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes YesTime-Varying Controls Yes Yes Yes Yes Yes Yes Yes YesControls for Larceny or Motor Theft Yes YesState-Specific Linear Time Trends Yes Yes
* Significant at the 10% level** Significant at the 5% level*** Significant at the 1% level
OLS - Weighted by State Population OLS - Unweighted
Log (Larceny Rate) Log (Larceny Rate)
Notes: Each column in each panel represents a separate regression. The unit of observation is state-year. Robust standard errors are clustered at the state level. Time-varying controls include policing and incarceration rates, welfare and public assistance spending, median income, poverty rate, unemployment rate, and demographics.
Log (Motor Vehicle Theft Rate) Log (Motor Vehicle Theft Rate)
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
37
Table 4: The Deterrence Effects of Castle Doctrine: Burglary, Robbery, and Aggravated Assault
1 2 3 4 5 6 7 8 9 10 11 12Panel A: BurglaryCastle Doctrine Law 0.0780*** 0.0290 0.0223 0.0164 0.0327* 0.0237 0.0572** 0.00961 0.00663 0.00277 0.00683 0.0207
(0.0255) (0.0236) (0.0223) (0.0247) (0.0165) (0.0207) (0.0272) (0.0291) (0.0268) (0.0304) (0.0222) (0.0259)
-0.0201 -0.0154(0.0139) (0.0214)
Panel B: RobberyCastle Doctrine Law 0.0408 0.0344 0.0262 0.0216 0.0376** 0.0515* 0.0448 0.0320 0.00839 0.00552 0.00874 0.0267
(0.0254) (0.0224) (0.0229) (0.0246) (0.0181) (0.0274) (0.0331) (0.0421) (0.0387) (0.0437) (0.0339) (0.0299)
-0.0156 -0.0115(0.0167) (0.0283)
Panel C: Aggravated AssaultCastle Doctrine Law 0.0434 0.0397 0.0372 0.0362 0.0424 0.0414 0.0555 0.0698 0.0343 0.0305 0.0341 0.0317
(0.0387) (0.0407) (0.0319) (0.0349) (0.0291) (0.0285) (0.0604) (0.0630) (0.0433) (0.0478) (0.0405) (0.0380)
-0.00343 -0.0150(0.0161) (0.0251)
Observations 550 550 550 550 550 550 550 550 550 550 550 550State and Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesRegion-by-Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes YesTime-Varying Controls Yes Yes Yes Yes Yes Yes Yes YesContemporaneous Crime Rates Yes YesState-Specific Linear Time Trends Yes Yes
* Significant at the 10% level** Significant at the 5% level*** Significant at the 1% level
OLS - Weighted by State Population OLS - Unweighted
Notes: Each column in each panel represents a separate regression. The unit of observation is state-year. Robust standard errors are clustered at the state level. Time-varying controls include policing and incarceration rates, welfare and public assistance spending, median income, poverty rate, unemployment rate, and demographics. Contemporaneous crime rates include larceny and motor vehicle theft rates.
Log (Burglary Rate) Log (Burglary Rate)
Log (Robbery Rate) Log (Robbery Rate)
Log (Aggravated Assault Rate) Log (Aggravated Assault Rate)
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
38
Table 5: The Effect of Castle Doctrine on Homicide
1 2 3 4 5 6Panel A: Log Homicide Rate (OLS - Weighted)
Castle Doctrine Law 0.0801** 0.0946*** 0.0937*** 0.0875** 0.0985*** 0.100**(0.0342) (0.0279) (0.0290) (0.0337) (0.0299) (0.0388)
-0.0212(0.0246)
Observations 550 550 550 550 550 550
Panel B: Log Homicide Rate (OLS - Unweighted)
Castle Doctrine Law 0.0877 0.0811 0.0600 0.0461 0.0580 0.0672(0.0638) (0.0769) (0.0684) (0.0764) (0.0662) (0.0450)
-0.0557(0.0494)
Observations 550 550 550 550 550 550
Panel C: Homicide (Negative Binomial - Unweighted)
Castle Doctrine Law 0.0565* 0.0734** 0.0879*** 0.0783** 0.0937*** 0.108***(0.0331) (0.0305) (0.0313) (0.0355) (0.0302) (0.0346)
-0.0352(0.0260)
Observations 550 550 550 550 550 550
Panel D: Log Murder Rate (OLS - Weighted)
Castle Doctrine Law 0.0906** 0.0955** 0.0916** 0.0884** 0.0981** 0.0813(0.0424) (0.0389) (0.0382) (0.0404) (0.0391) (0.0520)
-0.0110(0.0230)
Observations 550 550 550 550 550 550
State and Year Fixed Effects Yes Yes Yes Yes Yes YesRegion-by-Year Fixed Effects Yes Yes Yes Yes YesTime-Varying Controls Yes Yes Yes YesContemporaneous Crime Rates YesState-Specific Linear Time Trends Yes
* Significant at the 10% level** Significant at the 5% level*** Significant at the 1% level
Notes: Each column in each panel represents a separate regression. The unit of observation is state-year. Robust standard errors are clustered at the state level. Negative binomial estimates are interpreted in the same way as those in a log-linear OLS model. Time-varying controls include policing and incarceration rates, welfare and public assistance spending, median income, poverty rate, unemployment rate, and demographics. Contemporaneous crime rates include larceny and motor vehicle theft rates. Homicide data are from the published FBI Uniform Crime Reports, while murder data are from Return A of the FBI Master files.
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
39
Table 6: The Effect of Castle Doctrine on Felony-Type Homicide, Proportion of Robberies Committed Using a Gun, and Justifiable Homicide by Private Citizens
1 2 3 4 5 6Panel A: Log Felony-Type and Suspected Felony Type Homicides (OLS - Weighted)Castle Doctrine Law 0.0993 0.203* 0.220** 0.249** 0.222** 0.00121
(0.112) (0.109) (0.0907) (0.0992) (0.0871) (0.0686)
0.106(0.0648)
Observations 539 539 539 539 539 539Panel B: Proportion of Robberies Using Gun(OLS - Weighted)Castle Doctrine Law 0.0444*** 0.0218 0.0187 0.0227 0.0183 -0.00404
(0.0145) (0.0186) (0.0153) (0.0166) (0.0155) (0.0133)
0.0130(0.00823)
Observations 544 544 544 544 544 544Panel C: Justifiable Homicide by Private Citizens(OLS - Weighted, Dep. Variable = Count)Castle Doctrine Law 9.624*** 6.028** 4.550* 4.291 4.559* 0.835
(3.310) (2.450) (2.572) (2.936) (2.493) (1.802)
-0.854(2.006)
Observations 550 550 550 550 550 550Panel D: Justifiable Homicide by Private Citizens(OLS - Unweighted, Dep. Variable = Count)Castle Doctrine Law 4.328*** 3.370** 3.200** 2.908** 3.239** 0.960
(1.467) (1.300) (1.202) (1.350) (1.216) (1.219)
-1.168(1.223)
Observations 550 550 550 550 550 550Panel E: Justifiable Homicide by Private Citizens (Negative Binomial - Unweighted)Castle Doctrine Law 0.573*** 0.428* 0.283 0.219 0.324 NA
(0.210) (0.244) (0.235) (0.254) (0.228) NA
-0.253*(0.147)
Observations 550 550 550 550 550 550State and Year Fixed Effects Yes Yes Yes Yes Yes YesRegion-by-Year Fixed Effects Yes Yes Yes Yes YesTime-Varying Controls Yes Yes Yes YesContemporaneous Crime Rates YesState-Specific Linear Time Trends Yes
* Significant at the 10% level** Significant at the 5% level*** Significant at the 1% level
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
Notes: Each column in each panel represents a separate regression. The unit of observation is state-year. Robust standard errors are clustered at the state level. Negative binomial estimates are interpreted in the same way as those in a log-linear OLS model. Time-varying controls include policing and incarceration rates, welfare and public assistance spending, median income, poverty rate, unemployment rate, and demographics. Contemporaneous crime rates include larceny and motor vehicle theft rates. Homicide data are from the published FBI Uniform Crime Reports, while murder data are from Return A of the FBI Master files. NA indicates that the model did not converge. Castle doctrine states averaged 4.9 justifiable homicides in the year prior to enactment, or 10.0 if weighted by population.
40
Web Appendix Table A1: Differential Effects of Castle Doctrine Law by Treatment of Duty to Retreat and Civil Liability
Panel A: Effect of Castle Doctrine Law That Extends to Any Place One Has a Legal Right to Be
0.0263 0.0133 0.00903 0.0575* 0.0225 0.0547* 0.0668** 0.109*** 0.0347* -0.00381 4.697** 1.717(0.0266) (0.0218) (0.0267) (0.0286) (0.0286) (0.0314) (0.0289) (0.0352) (0.0186) (0.0140) (2.123) (2.023)
Observations 506 506 506 506 506 506 506 506 500 500 506 506
Panel B: Differential Effects by Whether the Law Includes a Presumption of Reasonableness
0.00622 0.0185 0.00912 0.0396 0.0262 0.0604** 0.0808*** 0.0831* 0.0353* -0.0125 5.806** -0.0621(0.0307) (0.0210) (0.0301) (0.0307) (0.0315) (0.0264) (0.0299) (0.0492) (0.0181) (0.0136) (2.652) (2.360)
0.0683*** 0.0188 0.0322 0.0699 0.0606 0.0215 0.0814 0.102 0.0266 0.0269 0.633 0.387(0.0202) (0.0296) (0.0261) (0.0429) (0.0395) (0.0524) (0.0545) (0.0615) (0.0242) (0.0296) (2.124) (2.133)
Observations 550 550 550 550 550 550 550 550 544 544 550 550
Panel C: Effect of Castle Doctrine Law, Excluding States That Did Not Also Remove Civil Liability
0.0310 0.0200 0.0183 0.0528* 0.0366 0.0433 0.0682** 0.0888** 0.0337** -0.000340 3.809* 0.157(0.0236) (0.0202) (0.0250) (0.0298) (0.0293) (0.0294) (0.0297) (0.0424) (0.0166) (0.0143) (2.092) (2.042)
Observations 517 517 517 517 517 517 517 517 511 511 517 517
State and Region-by-Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesTime-Varying Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesState-Specific Linear Time Trends Yes Yes Yes Yes Yes Yes
* Significant at the 10% level** Significant at the 5% level*** Significant at the 1% level
Justifiable Homicide by Private Citizens
(count)
Justifiable Homicide by Private Citizens
(count)
Castle Doctrine Law That Removes Duty to Retreat in Any Place
Log Burglary Rate
Proportion of Robberies with a
Gun
Proportion of Robberies with a
Gun
Log Homicide Rate
Log Homicide Rate
Log Aggravated Assault Rate
Log Robbery Rate
Castle Doctrine Law That Includes Presumption of Reasonableness
Other Castle Doctrine Law
Log Burglary Rate Log Robbery Rate Log Aggravated Assault Rate
Justifiable Homicide by Private Citizens
(count)
Notes: Each column in each panel represents a regression, each of which is weighted by state population. Robust standard errors are clustered at the state level. The unit of observation is state-year. Time-varying controls include policing and incarceration rates, welfare and public assistance spending, median income, poverty rate, unemployment rate, and demographics.
Castle Doctrine Law That Removes Civil Liability
Proportion of Robberies with a
GunLog Homicide RateLog Burglary Rate Log Robbery Rate Log Aggravated
Assault Rate
41
Table A2: Justifiable Homicide by Police
Panel A: OLS - Weighted, Dep. Variable = Count
Castle Doctrine Law 8.963* 2.770 1.252 0.621 1.182 1.129(4.501) (2.829) (2.600) (2.710) (2.643) (2.878)
-2.085*(1.094)
Observations 550 550 550 550 550 550Panel B: OLS - Unweighted, Dep. Variable = Count
Castle Doctrine Law 1.726 -0.244 -0.415 -0.643 -0.380 -0.352(1.836) (1.423) (1.372) (1.426) (1.374) (1.628)
-0.911(0.933)
Observations 550 550 550 550 550 550Panel C: Negative Binomial - Unweighted
Castle Doctrine Law 0.0328 -0.204** -0.208* -0.242** -0.193* -0.0751(0.164) (0.101) (0.107) (0.104) (0.104) (0.144)
-0.128(0.127)
Observations 550 550 550 550 550 550State and Year Fixed Effects Yes Yes Yes Yes Yes YesRegion-by-Year Fixed Effects Yes Yes Yes Yes YesTime-Varying Controls Yes Yes Yes YesContemporaneous Crime Rates YesState-Specific Linear Time Trends Yes
* Significant at the 10% level** Significant at the 5% level*** Significant at the 1% level
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
One Year Before Adoption of Castle Doctrine Law
Notes: Each column in each panel represents a separate regression. The unit of observation is state-year. Robust standard errors are clustered at the state level. Time-varying controls include policing and incarceration rates, welfare and public assistance spending, median income, poverty rate, unemployment rate, and demographics. Contemporaneous crime rates include larceny and motor vehicle theft rates. Homicide data are from the published FBI Uniform Crime Reports, while murder data are from Return A of the FBI Master files.